An introduction to ELECTRE Decision Making Method. This is an outranking method where options outranked each other such that the best option remains.Here both concordance and discordance index was used to find the outranking criteria.
An introduction on PROMETHEE for the students is provided.This PPT will try to explain the steps required to make a decision with the help of the method. Promethee is an outranking MCDM method.How to take a decision with the help of PROMETHEE Outranking MCDM technique ??
Project describes the use of Analytic hierarchy process (AHP) by taking bollywood songs of different era and finding the best song out of the listed options based on different parameters.
Decision Making Using The Analytic Hierarchy ProcessVaibhav Gaikwad
Analytic Hierarchy Process (AHP) is an
effective tool for dealing with complex decision making,
and may aid the decision maker to set priorities and
make the best decision. By reducing complex decisions
to a series of pairwise comparisons, and then
synthesizing the results, the AHP helps to capture both
subjective and objective aspects of a decision. In
addition, the AHP incorporates a useful technique for
checking the consistency of the decision maker’s
evaluations, thus reducing the bias in the decision
making process. In this paper we give special emphasis
to departure from consistency and its measurement and
to the use of absolute and relative measurement,
providing examples and justification for rank
preservation and reversal in relative measurement.
This is a special type of LPP in which the objective function is to find the optimum allocation of a number of tasks (jobs) to an equal number of facilities (persons). Here we make the assumption that each person can perform each job but with varying degree of efficiency. For example, a departmental head may have 4 persons available for assignment and 4 jobs to fill. Then his interest is to find the best assignment which will be in the best interest of the department.
An introduction on PROMETHEE for the students is provided.This PPT will try to explain the steps required to make a decision with the help of the method. Promethee is an outranking MCDM method.How to take a decision with the help of PROMETHEE Outranking MCDM technique ??
Project describes the use of Analytic hierarchy process (AHP) by taking bollywood songs of different era and finding the best song out of the listed options based on different parameters.
Decision Making Using The Analytic Hierarchy ProcessVaibhav Gaikwad
Analytic Hierarchy Process (AHP) is an
effective tool for dealing with complex decision making,
and may aid the decision maker to set priorities and
make the best decision. By reducing complex decisions
to a series of pairwise comparisons, and then
synthesizing the results, the AHP helps to capture both
subjective and objective aspects of a decision. In
addition, the AHP incorporates a useful technique for
checking the consistency of the decision maker’s
evaluations, thus reducing the bias in the decision
making process. In this paper we give special emphasis
to departure from consistency and its measurement and
to the use of absolute and relative measurement,
providing examples and justification for rank
preservation and reversal in relative measurement.
This is a special type of LPP in which the objective function is to find the optimum allocation of a number of tasks (jobs) to an equal number of facilities (persons). Here we make the assumption that each person can perform each job but with varying degree of efficiency. For example, a departmental head may have 4 persons available for assignment and 4 jobs to fill. Then his interest is to find the best assignment which will be in the best interest of the department.
Experience Mazda Zoom Zoom Lifestyle and Culture by Visiting and joining the Official Mazda Community at http://www.MazdaCommunity.org for additional insight into the Zoom Zoom Lifestyle and special offers for Mazda Community Members. If you live in Arizona, check out CardinaleWay Mazda's eCommerce website at http://www.Cardinale-Way-Mazda.com
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Dynamic Programming :
Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include:
Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions.
Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later.
Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones.
Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity.
Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics.
Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
This presentation covers the problems and solutions of North West Corner Method, Least cost Method and Vogels Approximation Method in Transportation Problem
Experience Mazda Zoom Zoom Lifestyle and Culture by Visiting and joining the Official Mazda Community at http://www.MazdaCommunity.org for additional insight into the Zoom Zoom Lifestyle and special offers for Mazda Community Members. If you live in Arizona, check out CardinaleWay Mazda's eCommerce website at http://www.Cardinale-Way-Mazda.com
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Dynamic Programming :
Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include:
Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions.
Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later.
Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones.
Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity.
Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics.
Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
This presentation covers the problems and solutions of North West Corner Method, Least cost Method and Vogels Approximation Method in Transportation Problem
128 Bit Parallel Prefix Tree Structure ComparatorIJRES Journal
A 128 bit comparator is designed with conventional digital cmos gates that make use of parallel
prefix tree structure. The comparison is performed bit wise proceeding from most significant bit to least
significant bit. The comparison of lower bits is carried only when the most significant bits are equal which
decreases the power dissipation in the circuit. To make the circuit regular the design is made using only cmos
logic gates. The transmission gates used in the existing design are replaced with the simple AND gates so that
the entire circuit can be designed in gate level. This 128 bit comparator is designed in cadence environment
using tsmc 0.18μm technology with a power dissipation of 0.28mw and with a delay of 0.09ms.
Chapter 2 Graphical Descriptions of Data 25 Chapter 2.docxcravennichole326
Chapter 2: Graphical Descriptions of Data
25
Chapter 2: Graphical Descriptions of Data
In chapter 1, you were introduced to the concepts of population, which again is a
collection of all the measurements from the individuals of interest. Remember, in most
cases you can’t collect the entire population, so you have to take a sample. Thus, you
collect data either through a sample or a census. Now you have a large number of data
values. What can you do with them? No one likes to look at just a set of numbers. One
thing is to organize the data into a table or graph. Ultimately though, you want to be able
to use that graph to interpret the data, to describe the distribution of the data set, and to
explore different characteristics of the data. The characteristics that will be discussed in
this chapter and the next chapter are:
1. Center: middle of the data set, also known as the average.
2. Variation: how much the data varies.
3. Distribution: shape of the data (symmetric, uniform, or skewed).
4. Qualitative data: analysis of the data
5. Outliers: data values that are far from the majority of the data.
6. Time: changing characteristics of the data over time.
This chapter will focus mostly on using the graphs to understand aspects of the data, and
not as much on how to create the graphs. There is technology that will create most of the
graphs, though it is important for you to understand the basics of how to create them.
Section 2.1: Qualitative Data
Remember, qualitative data are words describing a characteristic of the individual. There
are several different graphs that are used for qualitative data. These graphs include bar
graphs, Pareto charts, and pie charts.
Pie charts and bar graphs are the most common ways of displaying qualitative data. A
spreadsheet program like Excel can make both of them. The first step for either graph is
to make a frequency or relative frequency table. A frequency table is a summary of
the data with counts of how often a data value (or category) occurs.
Example #2.1.1: Creating a Frequency Table
Suppose you have the following data for which type of car students at a college
drive?
Ford, Chevy, Honda, Toyota, Toyota, Nissan, Kia, Nissan, Chevy, Toyota,
Honda, Chevy, Toyota, Nissan, Ford, Toyota, Nissan, Mercedes, Chevy,
Ford, Nissan, Toyota, Nissan, Ford, Chevy, Toyota, Nissan, Honda,
Porsche, Hyundai, Chevy, Chevy, Honda, Toyota, Chevy, Ford, Nissan,
Toyota, Chevy, Honda, Chevy, Saturn, Toyota, Chevy, Chevy, Nissan,
Honda, Toyota, Toyota, Nissan
Chapter 2: Graphical Descriptions of Data
26
A listing of data is too hard to look at and analyze, so you need to summarize it.
First you need to decide the categories. In this case it is relatively easy; just use
the car type. However, there are several cars that only have one car in the list. In
that case it is easier to make a category called other for the ones with low values.
Now ...
Scenario You are the VP of Franchise services for the Happy Buns .docxkaylee7wsfdubill
Scenario:
You are the VP of Franchise services for the Happy Buns Restaurant. You have been assigned the task of evaluation the best location for the next HB that a prospective franchisee has suggested in the Columbus, Ohio, area. You are using the standard template that provides for which criteria (attributes) you should evaluate. But the specific weights for these are open to adjustment depending on the specific area. These are the six criteria that you will use to evaluate this decision.
·
Close to drive through traffic – traffic counts (avg. thousands/day)
·
Property cost/investment and taxes = NPV of investment ($$)
·
Size of building (square feet in thousands)
·
Size of parking (max number of customers parking)
·
Insurance costs (thousands $ per year)
·
Ease of access from streets (subjective evaluation from observation)
There are five possible locations. You have collected the data from various sources including your VP Finance, Real estate agents, etc. This document summarizes the raw data for each of the five locations: Abberton, Bellview, Casstown, Denton, and Eddington, all suburbs of Columbus. See Data Below.
Assignment
Review the information and data regarding the different alternatives for restaurant location. Develop a MADM table with the raw data. Convert the raw data to utilities (scaled on 0 to 1). Determine the relative weights of each criteria. Evaluate the Decision Table for the best alternative. Do a sensitivity analysis.
Write a report to your boss, Executive VP. Explain your analysis and your recommendation. Provide a rationale for your decision including the logic you used to determine your weights.
Data
Download this Word doc with the data:
Happy Buns Raw Data.docx
Summary of Raw Data
for location of Happy Buns
in the Columbus, Ohio, area.
Criteria
Location
Traffic count (avg. thousands/day)
NPV of investment
($000,000)
Bldg. size (sq ft. 000)
Lot size
(Max customer parking)
Insurance
($000 / yr)
Access
(subjective)
Abberton
17
1.3
3.0
44
5.2
Good
Bellview
10
2.1
3.8
54
5.6
Excellent
Casstown
11
1.5
2.6
65
5.0
Fair
Denton
20
3.0
3.6
52
6.4
Poor
Eddington
15
2.8
4.2
50
6.3
Good
written report and Excel file
SLP Assignment Expectations
Analysis
·
Accurate, complete analysis (in Excel and Word) using the MADM model and theory.
Written Report
·
Length requirements =
2–3 pages minimum
(not including Cover and Reference pages)
·
Provide a brief introduction/ background of the problem.
·
Complete and accurate Excel analysis.
·
Written analysis that supports Excel analysis, and provides thorough discussion of assumptions, rationale, and logic used.
·
Complete, meaningful, and accurate recommendation(s).
MADM Model and Theory
Multi-Attribute Decision Making (MADM)
This decision method assumes certainty. In other words, there are no probabilities of future states to determine. And the data and costs are assumed to be known and accurate. The most common type of decision is a preference decision. Th.
1. (TCO 1) Which of the following sets of SQL clauses represent the minimum combination of clauses to make a working SQL statement? (Points : 5)
SELECT, WHERE
FROM, WHERE
SELECT, FROM
FROM, ORDER BY
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1. (TCO 1) Which of the following sets of SQL clauses represent the minimum combination of clauses to make a working SQL statement? (Points : 5)
SELECT, WHERE
FROM, WHERE
SELECT, FROM
FROM, ORDER BY
Introduction to Ant Colony Optimization TechniquesMrinmoy Majumder
Ant Colony Optimization is a metaheuristic algorithm inspired by the foraging behavior of ants. It involves simulating the way ants communicate and cooperate to find the shortest path between their nest and a food source.
History of Ant Colony Optimization
Ant Colony Optimization was first introduced by Marco Dorigo in the early 1990s, drawing inspiration from the pheromone trails that ants use to communicate with each other. Since then, it has been successfully applied to various optimization problems in computer science and engineering. It has proven to be particularly useful in solving routing problems, such as the traveling salesman problem.
Ant Colony Optimisation has been used in a wide range of applications, including routing optimisation, scheduling problems, and vehicle routing. By mimicking the collaborative and decentralised nature of ant colonies, this algorithm has proven to be effective in finding optimal solutions to complex problems.
For example, in routing optimisation, Ant Colony optimisation can be used to find the most efficient path for data packets to travel through a network by simulating how ants find the shortest path to a food source. This can help improve network efficiency and reduce congestion. Additionally, in vehicle routing applications, the algorithm can be used to optimise delivery routes for multiple vehicles by mimicking how ants communicate and coordinate with each other to efficiently explore and exploit different routes. This can ultimately lead to cost savings and faster delivery times.
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Seven Metaheuristics to Learn for your Next Data Science Project
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Introduction to Model Development for Prediction, Simulation, and Optimization.
https://imojo.in/1DJDUzm
Ten Ideas to open startups in smart agriculture.pptxMrinmoy Majumder
Smart agriculture, also known as precision agriculture, refers to the integration of advanced technologies and data analytics in agricultural practices to enhance productivity, efficiency, and sustainability. It involves the use of sensors, drones, satellite imagery, and Internet of Things (IoT) devices to collect real-time data on soil conditions, weather patterns, crop growth, and livestock health. By analyzing this data, farmers can make informed decisions regarding irrigation schedules, fertilizer application, pest control measures, and overall resource management. This transformative approach to farming not only maximizes yields but also minimizes environmental impact by optimizing resource utilization and reducing waste.
Startups play a crucial role in the agricultural sector by driving innovation and introducing new technologies that can revolutionize farming practices. These startups bring fresh ideas and solutions to address the challenges faced by farmers, such as increasing productivity, reducing costs, and ensuring sustainable practices. With their agility and entrepreneurial spirit, startups can quickly adapt to market demands and collaborate with farmers to develop customized solutions that meet their specific needs. Additionally, startups can also create job opportunities in rural areas and contribute to economic growth in the agricultural sector.
The purpose of this essay is to explore the role of startups in revolutionizing farming practices and the potential benefits they bring to the agricultural sector. By introducing new technologies and innovative solutions, startups can help farmers overcome challenges and achieve greater productivity while promoting sustainability. Additionally, the essay aims to highlight how startups can contribute to rural development by creating job opportunities and driving economic growth in farming communities.
When was the first bottled drinking water sold.pptxMrinmoy Majumder
Archimedes introduced fluid mechanics around 250 BC, stating the conservation of mass within a control volume for constant-density fluids. Isaac Newton described fluid viscosity in his 1687 Principia. Leonardo da Vinci studied fluid dynamics at Plato's Academy. The Reynolds theory explains the interaction between mass and viscosity in fluid mechanics.
Archimedes introduced fluid mechanics, an ancient Greek concept, around 250 BC. The first law of fluid mechanics, the conservation of mass, states that mass is conserved within a control volume for constant-density fluids. In his 1687 Principia, Isaac Newton, an ancient Greek, described fluid viscosity for the first time. Fluid dynamics can be traced back to Leonardo da Vinci, who studied at Plato's Academy. The Reynolds theory of fluid mechanics explains how mass and viscosity interact.
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Vulnerability Analysis of Wetlands under Changed Climate Scenarios with the h...Mrinmoy Majumder
Casestudy on Vulnerability Analysis of Wetlands under Changed Climate Scenarios with the help Water Cycle and Poly-Neural Networks.
Select the factors from literature and stakeholders survey to identify the most significant factors.
Separate these into two groups :
i)One group is for Reliability Enhancing Factors(R)
ii)Another group is for Risk Enhancing Factors(r)
Find the weightage of importance of each factors of each group with respect the impact of climate change on them. Use the Analytical Hierarchy Process Multi Criteria Decision Making method to determine the weightage. Here Climate Variables like Rainfall and Evapotranspiration can be selected as the criteria and all the factors as the alternative. Determine the weightage one group at a time.
Now place the sum of the value of the Reliability Enhancement Factors, (each multiplied with their weightage of importance) in Numerator and the sum of the value of Risk Enhancing Factor (each multiplied with their weightage of importance) in the Denominator.
To get the complete instructions enroll for the internshp at energyinstyle.website
1. The significance of addressing water and sanitation issues in developing countries: Explore how recent special issue calls for papers on water and sanitation reflect the urgent need to address these challenges in developing countries, where access to clean water and proper sanitation facilities is limited.
2. Examining innovative solutions:
3.Analyze the recent special issue calls for papers on water and sanitation to understand the emphasis on exploring innovative approaches and technologies that can improve access to clean water and sanitation facilities in developing countries.
4. Highlighting the impact on health and well-being: Shed light on the detrimental effects of inadequate water and sanitation on the health and well-being of individuals in developing countries. The special issue calls for papers aim to draw attention to the urgent need for action in order to prevent diseases and improve overall quality of life.
5. Encouraging collaboration and knowledge sharing: Emphasize the importance of collaboration between researchers, policymakers, and practitioners in finding sustainable solutions to water and sanitation challenges. The special issue calls for papers provide a platform for sharing knowledge, experiences, and best practices, fostering a global dialogue on addressing these pressing issues.
6. Promoting sustainable development goals: Discuss how the special issue calls for papers align with the United Nations Sustainable Development Goals
7.Emerging technologies for improving water quality: Discuss the latest research trends highlighted in the most recent special issue calls for papers, focusing on innovative technologies that aim to enhance water treatment processes and ensure better-quality drinking water for communities worldwide.
8. Sustainable approaches
Special Issues are available in different journals
1. Develop a smart irrigation system that utilizes sensors and data analytics to optimize water usage in agricultural fields, reducing water waste and improving crop yields.
2. Create a platform that connects farmers with precision agriculture technologies, such as drones and satellite imagery, to provide real-time monitoring and analysis of crop health and productivity.
3. Design a mobile application that enables farmers to remotely monitor and control their farm operations, including temperature, humidity, and nutrient levels in greenhouse environments.
4. Build an automated livestock monitoring system
In agricultural fields, data analytics is being used to optimise water usage, reduce waste, and increase crop yields. This technology is being combined with precision agriculture technologies to enable farmers to remotely monitor and control their farm operations.
But this article is not about the above ideas of starting startups but something more innovative is discussed.
Explore the latest advancements in hydro and energy informatics with seven ne...Mrinmoy Majumder
These special issue calls provide a unique opportunity for researchers and practitioners to contribute their work and contribute to the growing body of knowledge in hydro and energy informatics. Don't miss out on this chance to showcase your research and make a significant impact in these rapidly evolving fields. Discover cutting-edge research in hydro and energy informatics through seven exciting special issue calls by leading academic journals in the field. These special issue calls offer a platform for experts to share their innovative findings and insights, fostering collaboration and pushing the boundaries of hydro and energy informatics. Embrace this opportunity to stay at the forefront of advancements and contribute to shaping the future of these dynamic disciplines.
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An introductory explanation regarding the water cycle algorihtm.Complete tutorial can be found at www.baiatra.ws.The Water Cycle Algorithm is a nature-inspired optimization algorithm that mimics the movement and transformation of water in the Earth's hydrological cycle. It is based on the principles of evaporation, condensation, precipitation, and infiltration. This algorithm has gained popularity in solving complex optimization problems due to its ability to efficiently explore and exploit search spaces.
This algorithm is another famous metaheuristics or nature-based optimization technique proposed by Ali Sadollah in the year of 2015.
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What is the difference between Free and Paid Subscriber of HydroGeek Newslett...Mrinmoy Majumder
Free subscribers can only access Hydro Geek content for two weeks after it has been posted.Paid Subscribers do not have these limitations. Free subscribers are unable to leave comments on any of the posts, but paid subscribers can. The Restricted Post with premium content (posted just once per week) cannot be read by Free Subscribers. Paid Subscribers are permitted to notify our subscribers of their opportunities once per month.Free Subscribers lack this authorization, while Founding Members are permitted to post announcements twice a month. Paid subscribers also get a flat 25% discount on new publications like journals, books, and reports.
Ten Most Recognizable Case Studies of Using Outlier.pptxMrinmoy Majumder
Outlier detection is a method used to identify outliers in data, such as power consumption in non-residential buildings, online leakage detection in water distribution systems, and efficient water quality prediction systems.
It is also used in healthcare fraud, coastal water temperature data, and monitoring water quality data.
The method has been applied in various case studies, such as a survey, healthcare fraud, and fault detection for circulating water pumps.
It is also used in acoustic feature-based leakage event detection for large-scale water distribution networks.
Five Ideas for opening startups in Virtual and Green WaterMrinmoy Majumder
Start-ups can offer services for identifying ideal locations for virtual and green water harvesting tanks. Real-time monitoring of water levels is provided, ensuring optimal water management. Water classifiers, developed using water quality sensors, separate green water from virtual water, ensuring automatic detection of water use. Leak detection in irrigation pipelines or water tanks can save thousands of dollars and prevent major disasters. Non-linear AI-based computer models can predict the availability of virtual and green water, ensuring the watershed is well-stocked. These services can help optimize water management and reduce costs associated with traditional water sources.
The National Sea Grant College Program and the Water Power Technologies Office announced projects in Alaska, Guam, and Hawaiʻi that will examine how the adoption of ocean renewable energy could support sustainable energy systems."
"For island and remote communities in the United States, developing resilient electricity infrastructure and energy systems can be fraught with challenges. These locations often rely on expensive, unreliable energy systems that are vulnerable to volatile energy supplies and costs, natural disasters, and impacts from climate change. That’s why the National Oceanic and Atmospheric Administration’s (NOAA) National Sea Grant College Program, in partnership with the U.S. Department of Energy’s (DOE) Water Power Technologies Office, is supporting three projects in Alaska, Guam, and Hawaiʻi that will examine how adoption of ocean renewable energy could support sustainable energy systems. "
"Analysis by the Council on Energy, Environment and Water (CEEW) shows that over 45 percent of districts in India have undergone concerning changes to landscape."
"Cities, in particular, have witnessed disrupting natural drainage patterns and encroachment on vital water bodies such as lakes and ponds that were originally intended to absorb stormwater. For instance, Hyderabad, home to 400 lakes and 48 flood-abso..."
Groundwater is nature's insurance...World Bank Report
"As “nature’s insurance,” groundwater protects food security, reduces poverty, and boosts resilient economic growth, but the resource is threatened by overexploitation and pollution. High-level political action is needed to prioritize groundwater and align the private and social costs of its use. A new World Bank report considers the economic value of groundwater, the costs of misusing it, and the opportunities to leverage it more effectively."
"Revolutionary new Swiss 'water battery' will be one of Europe's main renewable sources of energy"
A Swiss company has built what is being called a giant water battery deep under the Alps that provides an energy storage capacity equivalent to 400,000 electric car batteries. It could be a game-changer.
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Latest Jobs and Scholarship Opportunities
Professor in Food, Water, Energy Nexus
Northeastern University’s College of Arts, Media and Design (CAMD) invites applications for open-rank, tenured or tenure-track positions in the thematic area of Food, Water, Energy Nexus.
South Africa Evaluator (Water, Energy, and/or Agriculture Sectors)
"Dexis is recruiting for an evaluator based in the South Central African region, with experience in the water, energy, and/or food sectors to be part of the WE4F Evaluation Team. This opportunity is a short-term technical assistance (STTA) consultant position and is contingent on USAID approval."
PhD Opportunity // Energy-Water-Nexus - Development of a modelling framework for integrated energy and water planning in water-scarce countries
The Inst
What is next in AI ML Modeling of Water Resource Development.pdfMrinmoy Majumder
In recent years like all the other fields of studies, the application of Artificial Intelligence and Machine Learning (AI&ML) on water resource development projects has increased manifold.
For example :
Sukanya, S., and Sabu Joseph. "Climate change impacts on water resources: An overview." Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence (2023): 55-76.
Kommadi, Bhagvan. "AI and ML Applications: 5G and 6G." (2023).
Joseph, Kiran, Ashok K. Sharma, Rudi van Staden, P. L. P. Wasantha, Jason Cotton, and Sharna Small. "Application of Software and Hardware-Based Technologies in Leaks and Burst Detection in Water Pipe Networks: A Literature Review." Water 15, no. 11 (2023): 2046.
Yurtsever, Mustafa, and E. M. E. Ç. Murat. "Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability." Ege Academic Review 23, no. 2 (2023): 265-278.
However, the uncertainty involved in Hydrologic/Hydraulic or Water Quality Parameters is very hard to simulate, and even with the advent of such cognitive algorithms accuracy and reliability of the models nevertheless lack substance. In this field of study, there is still much to be done. Some interesting objectives can be :
Very Short Term Course on MAUT in Water Resource Management.pdfMrinmoy Majumder
What is MAUT?
“Multi-attribute utility theory (MAUT) combines a class of psychological measurement models and scaling procedures which can be applied to the evaluation of alternatives which have multiple value relevant attributes.”Von Winterfeldt and Fischer (1975).
Some example applications of MAUT in Water Resource Management?
Feeny, David, William Furlong, George W. Torrance, Charles H. Goldsmith, Zenglong Zhu, Sonja DePauw, Margaret Denton, and Michael Boyle. "Multiattribute and single-attribute utility functions for the health utilities index mark 3 system." Medical care 40, no. 2 (2002): 113-128.
Zheng, Yong, and David Xuejun Wang. "Hybrid Multi-Criteria Preference Ranking by Subsorting." arXiv preprint arXiv:2306.11233 (2023).
Lopes, Yuri Gama, and Adiel Teixeira de Almeida. "Assessment of synergies for selecting a project portfolio in the petroleum industry based on a multi-attribute utility function." Journal of Petroleum Science and Engineering 126 (2015): 131-140.
Anand, Adarsh, Mohini Agarwal, Deepti Aggrawal, Laurie Hughes, Parisa Maroufkhani, and Yogesh K. Dwivedi. "Successive generation introduction time for high technological products: an analysis based on different multi-attribute utility functions." Environment, Development and Sustainability (2022): 1-18.
Latest Jobs, Scholarship Opportunities and CFPs in.pptxMrinmoy Majumder
Welcome to another edition of the Hydro Geek Newsletter. In this edition, I have collected some Jobs, Scholarship opportunities, and CFPs in the field of Hydroinformatics Engineering. The list is given next.
Latest Job Opportunites
Landscape Architect@ LOCUS Bengaluru, Karnataka, India
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Seven Techniques that you will learn when you enrol forMTech in Hydroinformat...Mrinmoy Majumder
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Five Case Studies. The detailed newsletter can be accessed at https://hydrogeek.substack.com/
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
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6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
6th International Conference on Machine Learning & Applications (CMLA 2024)
ELECTRE Decision Making Method
1. ELECTRE
Dr. Mrinmoy Majumder
Course Name : Intro to Multi Criteria Decision Making Methods
Lecture No.10 out of 15
https://opticlasses.teachable.com
Follow me on :
RG : Mrinmoy Majumder
Twitter : kuttu80
More such tutorials in http://www.baipatra.ws
Publish your original research in http://www.energyinstyle.website
2. ELECTRE
• ELECTRE is a family of multi-criteria decision analysis methods that
originated under the French School of decision making in the mid-
1960s.
• ELECTRE stands for: ELimination Et Choix Traduisant la REalité
(ELimination and Choice Expressing REality).
• The method was invented by Bernard Roy and his colleagues at
SEMA consultancy company.
3. Example of
ELECTRE
Decision Goal : To buy a car
Criteria : Cost and Speed
Alternatives : Mercedes Benz(M),
Jaguar(J), Toyota(T)
Aggregation Methods to be used :
ELECTRE
4. Step 1 : Development of the Alternative
Indicator Matrix
• First step of ELECTRE method is to create a Alternative-Indicator
Matrix :
Indicator
Cost (in
Lakh Rs.)
Speed (in km/hr)
Alternative
Mercedes Benz 80 200
Jaguar 100 300
Toyota 120 250
5. Step 2 : Development of the Normalized Indicator
Matrix or Normalized Decision Matrix
Matrix from Step 1 Indicator
Cost (in
Lakh Rs.)
Speed (in
km/hr)
Alternative
Merced
es Benz
80 200
Jaguar 100 300
Toyota 120 250
Square each value of the
indicators and add column
wise. Then find the square
root of the summation.
Divide each value of the
Indicators with the square
root.
Indicator
Cost (in
Lakh Rs.)
Speed (in
km/hr)
Alternative
Mercedes
Benz 6400 40000
Jaguar 10000 90000
Toyota 14400 62500
Column wise Sum 30800 192500
Square Root of the Sum 175.499 438.748
6. Step 2 : Contd.
• The Normalized Decision Matrix
Indicator
Cost Speed
Alternative
Mercedes
Benz
0.456 0.456
Jaguar 0.570 0.684
Toyota 0.684 0.570
7. Step 3 : Development of the Weighted
Normalized Decision Matrix
Indicator
Cost
(Weight
of
Indicator :
0.600)
Speed
(Weight
of
Indicator :
0.400)
Alternative
Mercedes
Benz
0.456 0.456
Jaguar 0.570 0.684
Toyota 0.684 0.570
Multiply the weight of
indicator of each
column with each value
of the alternatives for
that indicator to find
the weighted value of
the indicators for the
alternatives
Indicator
Cost
(Weight of
Indicator :
0.600)
Speed
(Weight of
Indicator :
0.400)
Alternative
Mercedes
Benz
0.274 0.182
Jaguar 0.342 0.274
Toyota 0.410 0.228
8. Step 4 : Development of the Concordance
Matrix Each alternative is compared with the
other alternative with respect to its
normalized value for the indicators.
If normalized value of M and J is
compared with respect to Cost indicator
then M < J, thus 0 is written. M is less
than J for Speed indicator as well. Thus
the value in the matrix will be 0.However
when J is compared with M, J>M for both
Cost and Speed Indicator. So the weight
of both the indicator will be added and
shown in that cell of the matrix.
Mercedes
Benz(M) Jaguar(J)
Toyota(T)
Mercedes Benz(M) 0 0 0
Jaguar(J) =0.6+0.4 0 =0+0.4
Toyota(T) =0.6+0.4 =0.6+0 0
Matrix from Step 3
Indicator
Cost
(Weight of
Indicator :
0.600)
Speed
(Weight of
Indicator :
0.400)
Alternative
Mercedes
Benz(M)
0.274 0.182
Jaguar(J) 0.342 0.274
Toyota(T) 0.410 0.228
9. Step 5 : Concordance Matrix
Mercedes
Benz Jaguar
Toyota
Mercedes Benz 0 0 0
Jaguar 1 0 0.4
Toyota 1 0.6 0
Column wise Sum
= 0+1+1 =
2
= 0+0+0.6 = 0.6 =0+0.4+0 = 0.4
Total : =2 + 0.6 + 0.4 = 3
Total/Number of Values in the Matrix = 3/4 = 0.75
Matrix from Step 4 Mercedes
Benz Jaguar
Toyota
Mercedes Benz 0 0 0
Jaguar =0.6+0.4 0 =0+0.4
Toyota 0.6+0.4 =0.6+0 0
1 2
3 4
Only the cell which depicts the
comparison between J with M,T with M,T
with J and J with T has real values. As a
result number of values in the matrix is 4
10. Step 5 : Contd.
Concordance Set : If C bar
(see last row of matrix 4) is less
than the value in the cell of the
matrix then the value will be
replaced by 1 otherwise if R is
greater than the real value in
the cell then 0 is used instead
of the existing value.
Mercedes
Benz Jaguar
Toyota
Mercedes Benz 0 0 0
Jaguar 1 0 0
Toyota 1 0 0
Matrix 4 Mercedes
Benz Jaguar
Toyota
Mercedes Benz 0 0 0
Jaguar 1 0 0.4
Toyota 1 0.6 0
Column wise
Sum
2 0.6 0.4
Total : =2 + 0.6 + 0.4 = 3
Total / (Number
of cells in the
Matrix where a
real number
exist) = C bar
= 3/4 = 0.75
11. Step 6 : Development of the Discordance
Matrix
Matrix from Step 3
Indicator
Cost
(Weight of
Indicator :
0.600)
Speed
(Weight of
Indicator :
0.400)
Alternative
Mercedes
Benz(M)
0.274 0.182
Jaguar(J) 0.342 0.274
Toyota(T) 0.410 0.228
The normalized value of each alternative
for each indicator is deducted from the
values of other alternatives for the same
indicator
Cost
Speed
M-J = 0.274 - 0.342 = 0.182 - 0.274
M-T = 0.274 - 0.410 = 0.182 - 0.228
J-M = 0.342 - 0.274 = 0.274 - 0.182
J-T = 0.342 - 0.410 = 0.274 - 0.228
T-M = 0.410 – 0.274 = 0.228 - 0.182
T-J = 0.410 – 0.342 = 0.228 - 0.274
12. Column : 1
The normalized value of each
alternative for each indicator is
deducted from the values of other
alternatives for the same indicator
Column : 2
Cost
Column : 3
Speed
Column : 4
Find the
maximum
value in the
row
(A)
Column : 5
Find the
maximum
negative
value or if
there is no
negative,
then use the
maximum
value of the
row(B)
Column :
6
(B)÷(A)
M-J -0.068 -0.091 0.091 0.091 1
M-T -0.137 -0.046 0.137 0.137 1
J-M 0.068 0.091 0.091 0.091 1
J-T -0.068 0.046 0.068 0.068 1
T-M 0.137 0.046 0.137 0.137 1
T-J 0.068 -0.046 0.068 0.046 0.667
Rough Set Matrix
13. Discordance Set : If D bar
(see last row of matrix 5) is less
than the value in the cell of the
matrix then the value will be
replaced by 1 otherwise if R is
greater than the real value in
the cell then 0 is used instead
of the existing value.
Mercedes
Benz Jaguar
Toyota
Mercedes Benz 0 1 1
Jaguar 1 0 1
Toyota 1 0 0
Matrix 5 : Matrix
from Step 4 can be
rewritten by using
the values from
Column 6 of Rough
Set Matrix
Mercedes
Benz(M) Jaguar(J)
Toyota(T)
Mercedes Benz(M) 0 1 1
Jaguar(J) 1 0 1
Toyota(T) 1 0.667 0
Column wise Sum 2 1.667 2
Total : =2 + 1.667 + 2 = 5.667
Total / (Number of
cells in the Matrix
where a real
number exist) = D
bar
= 5.667/6 = 0.945
14. Concordance
Set
(C)
Mercedes
Benz Jaguar
Toyota
Mercedes Benz 0 0 0
Jaguar 1 0 0
Toyota 1 0 0
C (AND or × ) D Mercedes
Benz
Jaguar Toyota
Mercedes Benz 0 0 0
Jaguar 1 0 0
Toyota 1 0 0
Discordance Set(D) Mercedes
Benz Jaguar
Toyota
Mercedes Benz 0 1 1
Jaguar 1 0 1
Toyota 1 0 0
0 0AND
or ×
= 0
1 1AND
or ×
= 1
AND
or ×
EXAMPLE
It Implies that :
J > M and T> M
Or
J and T > M
0 1AND
or ×
= 0